Classification Of Coronavirus Disease 2019 Research Articles

Ensemble learning based classifier to predict depression caused due to pandemic

Pandemic caused due to Corona Virus Disease 2019 (COVID-19) affected each and every person life throughout the world. First wave of COVID-19 followed by second wave made situation more panic. Government declared Lockdown imposed strict prohibition on social gathering, unnecessary outing, travelling, and education. During home quarantine, people shared opinion, expressed views, feelings on social media. Home isolation and quarantine affected mental health of people which may lead to depression. Hence in this research article depression is predicted by implementing Neural Network based model. At first level this model implements text classification of COVID-19 based Tweets. Neural network model accuracy is 86.85%. In next level, using same tweet dataset as input, Ensemble learning based model is constructed. This model uses one of the boosting techniques known as Adaboost. Model is executed by varying Train-test-validation ratio. It is observed that accuracy of the model is improved. The model showed accuracy of 99.33 % successfully in every execution. Obtained results are compared with previous work in same area.

Multi-feature Multi-Scale CNN-Derived COVID-19 Classification from Lung Ultrasound Data.

The global pandemic of the novel coronavirus disease 2019 (COVID-19) has put tremendous pressure on the medical system. Imaging plays a complementary role in the management of patients with COVID-19. Computed tomography (CT) and chest X-ray (CXR) are the two dominant screening tools. However, difficulty in eliminating the risk of disease transmission, radiation exposure and not being cost-effective are some of the challenges for CT and CXR imaging. This fact induces the implementation of lung ultrasound (LUS) for evaluating COVID-19 due to its practical advantages of noninvasiveness, repeatability, and sensitive bedside property. In this paper, we utilize a deep learning model to perform the classification of COVID-19 from LUS data, which could produce objective diagnostic information for clinicians. Specifically, all LUS images are processed to obtain their corresponding local phase filtered images and radial symmetry transformed images before fed into the multi-scale residual convolutional neural network (CNN). Secondly, image combination as the input of the network is used to explore rich and reliable features. Feature fusion strategy at different levels is adopted to investigate the relationship between the depth of feature aggregation and the classification accuracy. Our proposed method is evaluated on the point-of-care US (POCUS) dataset together with the Italian COVID-19 Lung US database (ICLUS-DB) and shows promising performance for COVID-19 prediction.

DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays.

The global pandemic of coronavirus disease 2019 (COVID-19) is continuing to have a significant effect on the well-being of the global population, thus increasing the demand for rapid testing, diagnosis, and treatment. As COVID-19 can cause severe pneumonia, early diagnosis is essential for correct treatment, as well as to reduce the stress on the healthcare system. Along with COVID-19, other etiologies of pneumonia and Tuberculosis (TB) constitute additional challenges to the medical system. Pneumonia (viral as well as bacterial) kills about 2 million infants every year and is consistently estimated as one of the most important factor of childhood mortality (according to the World Health Organization). Chest X-ray (CXR) and computed tomography (CT) scans are the primary imaging modalities for diagnosing respiratory diseases. Although CT scans are the gold standard, they are more expensive, time consuming, and are associated with a small but significant dose of radiation. Hence, CXR have become more widespread as a first line investigation. In this regard, the objective of this work is to develop a new deep transfer learning pipeline, named DenResCov-19, to diagnose patients with COVID-19, pneumonia, TB or healthy based on CXR images. The pipeline consists of the existing DenseNet-121 and the ResNet-50 networks. Since the DenseNet and ResNet have orthogonal performances in some instances, in the proposed model we have created an extra layer with convolutional neural network (CNN) blocks to join these two models together to establish superior performance as compared to the two individual networks. This strategy can be applied universally in cases where two competing networks are observed. We have tested the performance of our proposed network on two-class (pneumonia and healthy), three-class (COVID-19 positive, healthy, and pneumonia), as well as four-class (COVID-19 positive, healthy, TB, and pneumonia) classification problems. We have validated that our proposed network has been able to successfully classify these lung-diseases on our four datasets and this is one of our novel findings. In particular, the AUC-ROC are 99.60, 96.51, 93.70, 96.40% and the F1 values are 98.21, 87.29, 76.09, 83.17% on our Dataset X-Ray 1, 2, 3, and 4 (DXR1, DXR2, DXR3, DXR4), respectively.

Mix-and-Interpolate: A Training Strategy to Deal With Source-Biased Medical Data.

Till March 31st, 2021, the coronavirus disease 2019 (COVID-19) had reportedly infected more than 127 million people and caused over 2.5 million deaths worldwide. Timely diagnosis of COVID-19 is crucial for management of individual patients as well as containment of the highly contagious disease. Having realized the clinical value of non-contrast chest computed tomography (CT) for diagnosis of COVID-19, deep learning (DL) based automated methods have been proposed to aid the radiologists in reading the huge quantities of CT exams as a result of the pandemic. In this work, we address an overlooked problem for training deep convolutional neural networks for COVID-19 classification using real-world multi-source data, namely, the data source bias problem. The data source bias problem refers to the situation in which certain sources of data comprise only a single class of data, and training with such source-biased data may make the DL models learn to distinguish data sources instead of COVID-19. To overcome this problem, we propose MIx-aNd-Interpolate (MINI), a conceptually simple, easy-to-implement, efficient yet effective training strategy. The proposed MINI approach generates volumes of the absent class by combining the samples collected from different hospitals, which enlarges the sample space of the original source-biased dataset. Experimental results on a large collection of real patient data (1,221 COVID-19 and 1,520 negative CT images, and the latter consisting of 786 community acquired pneumonia and 734 non-pneumonia) from eight hospitals and health institutions show that: 1) MINI can improve COVID-19 classification performance upon the baseline (which does not deal with the source bias), and 2) MINI is superior to competing methods in terms of the extent of improvement.

A Comprehensive Survey of COVID-19 Detection Using Medical Images.

The outbreak of the Coronavirus disease 2019 (COVID-19) caused the death of a large number of people and declared as a pandemic by the World Health Organization. Millions of people are infected by this virus and are still getting infected every day. As the cost and required time of conventional Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests to detect COVID-19 is uneconomical and excessive, researchers are trying to use medical images such as X-ray and Computed Tomography (CT) images to detect this disease with the help of Artificial Intelligence (AI)-based systems, to assist in automating the scanning procedure. In this paper, we reviewed some of these newly emerging AI-based models that can detect COVID-19 from X-ray or CT of lung images. We collected information about available research resources and inspected a total of 80 papers till June 20, 2020. We explored and analyzed data sets, preprocessing techniques, segmentation methods, feature extraction, classification, and experimental results which can be helpful for finding future research directions in the domain of automatic diagnosis of COVID-19 disease using AI-based frameworks. It is also reflected that there is a scarcity of annotated medical images/data sets of COVID-19 affected people, which requires enhancing, segmentation in preprocessing, and domain adaptation in transfer learning for a model, producing an optimal result in model performance. This survey can be the starting point for a novice/beginner level researcher to work on COVID-19 classification.

Interobserver Agreement in the Analysis of the Different Radiological Classification of COVID-19 on Computed Tomography

Introduction: Computed tomography (CT) has approximately 98% sensitivity for Coronavirus disease-2019 (COVID-19). Various algorithms were designed using CT images. However, the interobserver agreement of different radiological classifications of COVID-19 is not yet known. Thus, this study aimed to investigate the interobserver agreement of different radiological classifications of COVID-19. Materials and Methods: This study included 212 patients who were positive on the polymerase chain reaction test and eligible for CT. Four radiologists examined all CT images simultaneously. They reached a consensus that CT images can provide definite findings of COVID-19. The Radiological Society of North America (RSNA) consensus statement, the British Society of Thoracic Imaging (BSTI) structured reporting statement, and COVID-19 Reporting and Data System (CO-RADS) were used. Fleiss' Kappa was used to detect interobserver agreement. Kappa values of 0.00-0.20 were considered as slight, 0.21-0.40 as fair, 0.41-0.60 as moderate, 0.61-0.80 as substantial, and 0.81-1.00 as near-perfect agreement, and p<0.05 was accepted as significant.

Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images.

Literature survey shows that convolutional neural network (CNN)-based pretrained models have been largely used for CoronaVirus Disease 2019 (COVID-19) classification using chest X-ray (CXR) and computed tomography (CT) datasets. However, most of the methods have used a smaller number of data samples for both CT and CXR datasets for training, validation, and testing. As a result, the model might have shown good performance during testing, but this type of model will not be more effective on unseen COVID-19 data samples. Generalization is an important term to be considered while designing a classifier that can perform well on completely unseen datasets. Here, this work proposes a large-scale learning with stacked ensemble meta-classifier and deep learning-based feature fusion approach for COVID-19 classification. The features from the penultimate layer (global average pooling) of EfficientNet-based pretrained models were extracted and the dimensionality of the extracted features reduced using kernel principal component analysis (PCA). Next, a feature fusion approach was employed to merge the features of various extracted features. Finally, a stacked ensemble meta-classifier-based approach was used for classification. It is a two-stage approach. In the first stage, random forest and support vector machine (SVM) were applied for prediction, then aggregated and fed into the second stage. The second stage includes logistic regression classifier that classifies the data sample of CT and CXR into either COVID-19 or Non-COVID-19. The proposed model was tested using large CT and CXR datasets, which are publicly available. The performance of the proposed model was compared with various existing CNN-based pretrained models. The proposed model outperformed the existing methods and can be used as a tool for point-of-care diagnosis by healthcare professionals.

COVID-19 classification of X-ray images using deep neural networks.

ObjectivesIn the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in diagnosis and monitoring of patients with COVID-19. We propose a deep learning model for detection of COVID-19 from CXRs, as well as a tool for retrieving similar patients according to the model’s results on their CXRs. For training and evaluating our model, we collected CXRs from inpatients hospitalized in four different hospitals.MethodsIn this retrospective study, 1384 frontal CXRs, of COVID-19 confirmed patients imaged between March and August 2020, and 1024 matching CXRs of non-COVID patients imaged before the pandemic, were collected and used to build a deep learning classifier for detecting patients positive for COVID-19. The classifier consists of an ensemble of pre-trained deep neural networks (DNNS), specifically, ReNet34, ReNet50¸ ReNet152, and vgg16, and is enhanced by data augmentation and lung segmentation. We further implemented a nearest-neighbors algorithm that uses DNN-based image embeddings to retrieve the images most similar to a given image.ResultsOur model achieved accuracy of 90.3%, (95% CI: 86.3–93.7%) specificity of 90% (95% CI: 84.3–94%), and sensitivity of 90.5% (95% CI: 85–94%) on a test dataset comprising 15% (350/2326) of the original images. The AUC of the ROC curve is 0.96 (95% CI: 0.93–0.97).ConclusionWe provide deep learning models, trained and evaluated on CXRs that can assist medical efforts and reduce medical staff workload in handling COVID-19.Key Points• A machine learning model was able to detect chest X-ray (CXR) images of patients tested positive for COVID-19 with accuracy and detection rate above 90%.• A tool was created for finding existing CXR images with imaging characteristics most similar to a given CXR, according to the model’s image embeddings.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00330-021-08050-1.

A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images.

The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently.

The characteristics and evolution of pulmonary fibrosis in COVID-19 patients as assessed by AI-assisted chest HRCT.

The characteristics and evolution of pulmonary fibrosis in patients with coronavirus disease 2019 (COVID-19) have not been adequately studied. AI-assisted chest high-resolution computed tomography (HRCT) was used to investigate the proportion of COVID-19 patients with pulmonary fibrosis, the relationship between the degree of fibrosis and the clinical classification of COVID-19, the characteristics of and risk factors for pulmonary fibrosis, and the evolution of pulmonary fibrosis after discharge. The incidence of pulmonary fibrosis in patients with severe or critical COVID-19 was significantly higher than that in patients with moderate COVID-19. There were significant differences in the degree of pulmonary inflammation and the extent of the affected area among patients with mild, moderate and severe pulmonary fibrosis. The IL-6 level in the acute stage and albumin level were independent risk factors for pulmonary fibrosis. Ground-glass opacities, linear opacities, interlobular septal thickening, reticulation, honeycombing, bronchiectasis and the extent of the affected area were significantly improved 30, 60 and 90 days after discharge compared with at discharge. The more severe the clinical classification of COVID-19, the more severe the residual pulmonary fibrosis was; however, in most patients, pulmonary fibrosis was improved or even resolved within 90 days after discharge.

Analysis of Predictive Factors for Moderate-Stage COVID-19 Outcome and Maximal Extent of Lung Injury

Background: As the coronavirus disease 2019 (COVID-19) epidemic continues to spread, it is important to predict the clinical classification of COVID-19 and evaluate the progression of lung injury. Objectives: To investigate the predictive factors of the outcome of moderate-stage coronavirus disease 2019 (COVID-19) and maximal extent of lung injury. Patients and Methods: This study was a retrospective analysis of 97 patients with moderate-stage COVID-19 diagnosed in our hospital. We divided the patients into two groups according to disease progression: one group for moderate stage and another for both severe stage and critically severe stage COVID-19. We then analyzed the independent factors influencing changes in the course of the disease in moderate-stage patients using binary logistic regression. Next, we assessed the computed tomography (CT) score of maximal lung injury using follow-up images of the patients. We used multiple linear regression (MLR) to analyze the independent variables, and to predict the CT score of maximal lung injury in COVID-19 patients. Results: The results were obtained using multivariate logistic regression analysis, and the independent factors affecting clinical classification were baseline CT score (P = 0.008), high-sensitivity C-reactive protein (hs-CRP) (P = 0.001), and diabetes (P = 0.04). MLR revealed that the factors predicting the extent of maximal lung injury in COVID-19 patients were age (P = 0.014), neutrophil percentage (P = 0.038), lymphocyte percentage (P = 0.031), hs-CRP (P = 0.010), and baseline CT score (P &lt; 0.001). The optimal cut-off value of hs-CRP was 18.5, and the baseline CT score was 8.5. Conclusion: Age, baseline CT score, hs-CRP, neutrophil percentage, and lymphocyte percentage could predict the CT score of maximal lung injury, and hs-CRP &gt; 18.5, baseline CT score ≥ 9, and diabetes were independent factors of severe/critically severe COVID-19.

Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images.

Coronaviruses are a family of viruses that majorly cause respiratory disorders in humans. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new strain of coronavirus that causes the coronavirus disease 2019 (COVID-19). WHO has identified COVID-19 as a pandemic as it has spread across the globe due to its highly contagious nature. For early diagnosis of COVID-19, the reverse transcription-polymerase chain reaction (RT-PCR) test is commonly done. However, it suffers from a high false-negative rate of up to 67% if the test is done during the first five days of exposure. As an alternative, research on the efficacy of deep learning techniques employed in the identification of COVID-19 disease using chest X-ray images is intensely pursued.As pneumonia and COVID-19 exhibit similar/ overlapping symptoms and affect the human lungs, a distinction between the chest X-ray images of pneumonia patients and COVID-19 patients becomes challenging. In this work, we have modeled the COVID-19 classification problem as a multiclass classification problem involving three classes, namely COVID-19, pneumonia, and normal. We have proposed a novel classification framework which combines a set of handpicked features with those obtained from a deep convolutional neural network. The proposed framework comprises of three modules. In the first module, we exploit the strength of transfer learning using ResNet-50 for training the network on a set of preprocessed images and obtain a vector of 2048 features. In the second module, we construct a pool of frequency and texture based 252 handpicked features that are further reduced to a set of 64 features using PCA. Subsequently, these are passed to a feed forward neural network to obtain a set of 16 features. The third module concatenates the features obtained from first and second modules, and passes them to a dense layer followed by the softmax layer to yield the desired classification model. We have used chest X-ray images of COVID-19 patients from four independent publicly available repositories, in addition to images from the Mendeley and Kaggle Chest X-Ray Datasets for pneumonia and normal cases.To establish the efficacy of the proposed model, 10-fold cross-validation is carried out. The model generated an overall classification accuracy of 0.974 0.02 and a sensitivity of 0.987 0.05, 0.963 0.05, and 0.973 0.04 at 95% confidence interval for COVID-19, normal, and pneumonia classes, respectively. To ensure the effectiveness of the proposed model, it was validated using an independent Chest X-ray cohort and an overall classification accuracy of 0.979 was achieved. Comparison of the proposed framework with state-of-the-art methods reveal that the proposed framework outperforms others in terms of accuracy and sensitivity. Since interpretability of results is crucial in the medical domain, the gradient-based localizations are captured using Gradient-weighted Class Activation Mapping (Grad-CAM). In summary, the results obtained are stable over independent cohorts and interpretable using Grad-CAM localizations that serve as clinical evidence.

COVID-19-induced endotheliitis: emerging evidence and possible therapeutic strategies.

The coronavirus disease 2019 (COVID‐19) pandemic, a viral illness caused by the severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2), 1 has produced at the time of this writing nearly 33 million cases of infection, with over a million deaths in 235 countries, 2 causing an unprecedented burden on healthcare systems and a severe global socioeconomic crisis. As the pandemic spreads, knowledge on the disease course, as well as potential risk factors and predictors of severity is increasing daily, and initial data from randomised controlled studies have allowed care providers to refine therapeutic strategies. Nonetheless, mortality is markedly elevated among those presenting with severe disease, long‐term sequelae among survivors are unknown, and vaccine‐based therapies currently remain at early stages of development. Most reported cases are asymptomatic or present with mild symptoms; however, 7–26% of hospitalised patients experience severe disease, often requiring admission to intensive care units (ICUs), with progressive multiple organ dysfunction and high mortality. 3 , 4 , 5 Such differences in clinical outcomes have led physicians to initiate diverse pharmacological therapies at various stages of the disease, generating challenges as to the most appropriate therapeutic choice for COVID‐19. In this context, the use of dexamethasone has significantly reduced mortality rates in critically ill patients requiring supplemental oxygen or mechanical ventilation, 6 and remdesivir has demonstrated clinical benefit in hospitalised patients, but with unknown survival benefit to date 7 ; additional effective treatment options are therefore urgently needed. In an initial attempt to provide a uniform and widely reproducible methodology to guide systematic treatment strategies, a three‐stage classification of COVID‐19 has been proposed. 8 The Stage I or ‘early infection’ occurs at the initial establishment of disease with high viral replication, and commonly presents with a range of complaints that can include mild and often non‐specific influenza‐like signs and symptoms. Stage II is the ‘pulmonary phase’, with preferential viral‐mediated injury of the lung parenchyma and this is characterised by shortness of breath, hypoxia and pulmonary infiltrates with some degree of lung inflammation. Stage III is characterised by an exaggerated host immune‐inflammatory response to the virus, leading to acute respiratory distress syndrome (ARDS) and multi‐organ failure (MOF).

Design of an Online Detection System for COVID-19 based on Attention Mechanism

Corona Virus Disease 2019 (COVID-19) is an acute respiratory infection caused by the 2019 novel coronavirus infection that was discovered in some hospitals in Wuhan, China in December 2019. From a global perspective, COVID-19 epidemic is still in a pandemic period. Every country is taking timely preventive measures. Therefore, it is necessary to use Artificial Intelligence (AI) identification to assist radiologists in diagnosing COVID-19. This paper proposes a network model of dynamic self-attention machine for pneumonia images. The model adopts DPN92 and GCNet. It implements CR images and CT image classification of COVID-19. A COVID-19 detection system based on dynamic auto-attention machine is designed. The lung CT or CR images that meet the design requirements will be uploaded through the browser, and the background server will analyze and process the lung images, and finally output the detection category of the lung images.

Which Factors, Smoking, Drinking Alcohol, Betel Quid Chewing, or Underlying Diseases, Are More Likely to Influence the Severity of COVID-19?

The global outbreak of the coronavirus disease 2019 (COVID-19) pandemic occurred in late 2019 and early 2020. The factors that influence disease severity should be of clinical concern. Existing findings on the effects of smoking on COVID-19 are also controversial and need to be confirmed by further research. In addition, the effects of alcohol consumption and betel quid (BQ) chewing on COVID-19 are unclear. The aim of this study was to examine the demographic characteristics of COVID-19 patients and the effects of smoking, drinking, BQ chewing, and underlying diseases on the severity of COVID-19. A retrospective study was conducted on 91 patients with confirmed cases of COVID-19 hospitalized in Yueyang, Hunan Province, China from 21 January to 8 March, 2020. Patient demographic data, and information on smoking, drinking and BQ chewing, and underlying diseases were extracted from the patient electronic medical records (EMR) and telephone interviews. The chi-square test was used to conduct a univariate analysis of the factors influencing the severity of COVID-19, and ordinal logistic regression analysis was used to identify the factors related to the severity of COVID-19. The results showed that the rates of smoking, drinking and BQ chewing were 15.4, 26.4, and 7.1%, respectively, there was no significant relationship between these lifestyle factors and the severity of COVID-19 (P > 0.05). However, underlying diseases such as diabetes [odds ratio (OR) = 7.740, 95% confidence interval (CI):1.000–60.740, P = 0.050], source of infection (OR = 0.180, 95% CI: 0.030–0.980, P = 0.049), and employment status (retired/unemployed vs. employed: OR = 29.430, 95% CI, 1.050 – 822.330, P = 0.047) were significant independent predictors of severe COVID-19 infection. These individuals should be informed of methods to increase personal protection, and doctors should prevent these individuals from developing serious diseases. It is important to pay attention to the source of infection and timely medical treatment. This study showed that the clinical classification of COVID-19 was associated with patients with diabetes, source of infection, and retired/unemployed. Therefore in the clinical practice of COVID-19 should be more concern these factors. Although no statistical significance was found in smoking, drinking alcohol, BQ chewing, and severity of COVID-19 patients, more studies have confirmed that are harmful and risk factors for underlying diseases in the population. Health authorities should formulate policies to publicize the harmful effects of smoking, drinking, and betel nut chewing and promote a healthy lifestyle.

An Ensemble of Global and Local-Attention Based Convolutional Neural Networks for COVID-19 Diagnosis on Chest X-ray Images

The recent Coronavirus Disease 2019 (COVID-19) pandemic has put a tremendous burden on global health systems. Medical practitioners are under great pressure for reliable screening of suspected cases employing adjunct diagnostic tools to standard point-of-care testing methodology. Chest X-rays (CXRs) are appearing as a prospective diagnostic tool with easy-to-acquire, low-cost and less cross-contamination risk features. Artificial intelligence (AI)-attributed CXR evaluation has shown great potential for distinguishing COVID-19-induced pneumonia from other associated clinical instances. However, one of the associated challenges with diagnostic imaging-based modeling is incorrect feature attribution, which leads the model to learn misguiding disease patterns, causing wrong predictions. Here, we demonstrate an effective deep learning-based methodology to mitigate the problem, thereby allowing the classification algorithm to learn from relevant features. The proposed deep-learning framework consists of an ensemble of convolutional neural network (CNN) models focusing on both global and local pathological features from CXR lung images, while the latter is extracted using a multi-instance learning scheme and a local attention mechanism. An inspection of a series of backbone CNN models using global and local features, and an ensemble of both features, trained from high-quality CXR images of 1311 patients, further augmented for achieving the symmetry in class distribution, to localize lung pathological features followed by the classification of COVID-19 and other related pneumonia, shows that a DenseNet161 architecture outperforms all other models, as evaluated on an independent test set of 159 patients with confirmed cases. Specifically, an ensemble of DenseNet161 models with global and local attention-based features achieve an average balanced accuracy of 91.2%, average precision of 92.4%, and F1-score of 91.9% in a multi-label classification framework comprising COVID-19, pneumonia, and control classes. The DenseNet161 ensembles were also found to be statistically significant from all other models in a comprehensive statistical analysis. The current study demonstrated that the proposed deep learning-based algorithm can accurately identify the COVID-19-related pneumonia in CXR images, along with differentiating non-COVID-19-associated pneumonia with high specificity, by effectively alleviating the incorrect feature attribution problem, and exploiting an enhanced feature descriptor.

Chest X-ray image phase features for improved diagnosis of COVID-19 using convolutional neural network.

Purpose:Recently, the outbreak of the novel coronavirus disease 2019 (COVID-19) pandemic has seriously endangered human health and life. In fighting against COVID-19, effective diagnosis of infected patient is critical for preventing the spread of diseases. Due to limited availability of test kits, the need for auxiliary diagnostic approach has increased. Recent research has shown radiography of COVID-19 patient, such as CT and X-ray, contains salient information about the COVID-19 virus and could be used as an alternative diagnosis method. Chest X-ray (CXR) due to its faster imaging time, wide availability, low cost, and portability gains much attention and becomes very promising. In order to reduce intra- and inter-observer variability, during radiological assessment, computer-aided diagnostic tools have been used in order to supplement medical decision making and subsequent management. Computational methods with high accuracy and robustness are required for rapid triaging of patients and aiding radiologist in the interpretation of the collected data. Method:In this study, we design a novel multi-feature convolutional neural network (CNN) architecture for multi-class improved classification of COVID-19 from CXR images. CXR images are enhanced using a local phase-based image enhancement method. The enhanced images, together with the original CXR data, are used as an input to our proposed CNN architecture. Using ablation studies, we show the effectiveness of the enhanced images in improving the diagnostic accuracy. We provide quantitative evaluation on two datasets and qualitative results for visual inspection. Quantitative evaluation is performed on data consisting of 8851 normal (healthy), 6045 pneumonia, and 3323 COVID-19 CXR scans. Results:In Dataset-1, our model achieves 95.57% average accuracy for a three classes classification, 99% precision, recall, and F1-scores for COVID-19 cases. For Dataset-2, we have obtained 94.44% average accuracy, and 95% precision, recall, and F1-scores for detection of COVID-19.Conclusions:Our proposed multi-feature-guided CNN achieves improved results compared to single-feature CNN proving the importance of the local phase-based CXR image enhancement. Future work will involve further evaluation of the proposed method on a larger-size COVID-19 dataset as they become available.

The RSNA International COVID-19 Open Radiology Database (RICORD).

The coronavirus disease 2019 (COVID-19) pandemic is a global health care emergency. Although reverse-transcription polymerase chain reaction testing is the reference standard method to identify patients with COVID-19 infection, chest radiography and CT play a vital role in the detection and management of these patients. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making. However, inadequate availability of a diverse annotated data set has limited the performance and generalizability of existing models. To address this unmet need, the RSNA and Society of Thoracic Radiology collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD). This database is the first multi-institutional, multinational, expert-annotated COVID-19 imaging data set. It is made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. Pixel-level volumetric segmentation with clinical annotations was performed by thoracic radiology subspecialists for all COVID-19–positive thoracic CT scans. The labeling schema was coordinated with other international consensus panels and COVID-19 data annotation efforts, the European Society of Medical Imaging Informatics, the American College of Radiology, and the American Association of Physicists in Medicine. Study-level COVID-19 classification labels for chest radiographs were annotated by three radiologists, with majority vote adjudication by board-certified radiologists. RICORD consists of 240 thoracic CT scans and 1000 chest radiographs contributed from four international sites. It is anticipated that RICORD will ideally lead to prediction models that can demonstrate sustained performance across populations and health care systems.© RSNA, 2021Online supplemental material is available for this article.See also the editorial by Bai and Thomasian in this issue.

JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation

Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnostic tool is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Still, the sensitivity of the RT-PCR test is not high enough to effectively prevent the pandemic. The chest CT scan test provides a valuable complementary tool to the RT-PCR test, and it can identify the patients in the early-stage with high sensitivity. However, the chest CT scan test is usually time-consuming, requiring about 21.5 minutes per case. This paper develops a novel Joint Classification and Segmentation (JCS) system to perform real-time and explainable COVID- 19 chest CT diagnosis. To train our JCS system, we construct a large scale COVID- 19 Classification and Segmentation (COVID-CS) dataset, with 144,167 chest CT images of 400 COVID- 19 patients and 350 uninfected cases. 3,855 chest CT images of 200 patients are annotated with fine-grained pixel-level labels of opacifications, which are increased attenuation of the lung parenchyma. We also have annotated lesion counts, opacification areas, and locations and thus benefit various diagnosis aspects. Extensive experiments demonstrate that the proposed JCS diagnosis system is very efficient for COVID-19 classification and segmentation. It obtains an average sensitivity of 95.0% and a specificity of 93.0% on the classification test set, and 78.5% Dice score on the segmentation test set of our COVID-CS dataset. The COVID-CS dataset and code are available at https://github.com/yuhuan-wu/JCS.

Modified VGG deep-learning architecture for COVID-19 classification using chest radiography images

Background: The world faced a deadly disease encounter by the starting of 2020, known as coronavirus disease 2019 (COVID-19). Due to the rapid increase in the counts of COVID cases, the WHO declared the COVID-19 as a pandemic on March 11, 2020. Among the different screening techniques available for COVID-19, radiography of the chest is one of the efficient way for disease detection. While other disease detection techniques take time, radiography takes less time to identify because of the abnormalities caused by the disease in the lungs. Methods: In the rapid development era of artificial intelligence and deep-learning techniques, various models are being developed for COVID disease detection. COVID-19 can be easily detected from Chest X-ray images and the pretrained models yield high accuracy with small dataset. Results: In this paper, one of the standard deep-learning architectures, VGGNet, is modified for classifying chest X-ray images under four categories. The proposed model uses open source dataset that contains 231, 2503, 1345, and 1341 images of four classes such as COVID, bacterial, normal, and viral chest radiography images, respectively. Conclusion: The performance matrices of the proposed work were compared with the five benchmark deep-learning architectures namely VGGNet, AlexNet, GoogLeNET, Inception-v4, and DenseNet-201.